Ensemble approach for developing a smart heart disease prediction system using classification algorithms

Clicks: 367
ID: 6736
2018
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #18 of 242 articles by views in research reports in clinical cardiology

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 242 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Ensemble approach for developing a smart heart disease prediction system using classification algorithms Mustafa Jan,1 Akber A Awan,2 Muhammad S Khalid,1 Salman Nisar1 1Department of Industrial and Manufacturing Engineering, Pakistan Navy Engineering College, National University of Sciences and Technology, Karachi, Pakistan; 2Department of Management and Information System, Pakistan Navy Engineering College, National University of Sciences and Technology, Karachi, Pakistan Abstract: In health care informatics, the predictive modeling solution for cardiovascular risk estimation is extremely challenging. Thus, the attempt to clinically screen the medical databases and predictive modeling through soft computing tools is regarded as a valuable and economical option for medical practitioners. Therefore, the soft computing tools are today’s need in health care application, which can perform data analysis and modeling, and they can assist the physician to make right and prompt clinical decisions. Extracting patterns that tie predictor’s variables in a health science database is the topic of data mining. Existing data mining techniques are appropriate to model complex, dynamic processes. In this work, we propose Ensemble model approach for combining the predictive ability of a multiple classifiers’ model for better prediction accuracy. In this study, ensemble learning combines five classifiers’ model approaches, including support vector machine, artificial neural network, Naïve Bayesian, regression analysis, and random forest, to predict and diagnose the recurrence of cardiovascular disease. The cardiovascular data records of Cleveland and Hungarian were extracted from the UCI repository. Experimental results demonstrated that the ensemble model is a superior approach in terms of high predictive accuracy and reliability of diagnostics performance. In addition to this, this study also presents a smart heart disease prediction system as a valuable, economical and prompt predictive option having friendly graphical user interface, which is scalable and expandable. Keywords: ensemble methods, smart heart disease prediction system, data mining model, classification techniques
Reference Key
mustafa2018ensembleresearch Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mustafa Jan;Akber A Awan;Muhammad S Khalid;Salman Nisar and
Journal research reports in clinical cardiology
Year 2018
DOI
10.2147/RRCC.S172035
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.